A condense study was done to compare between the ordinary estimators. In particular the maximum likelihood estimator and the robust estimator, to estimate the parameters of the mixed model of order one, namely ARMA(1,1) model.

Simulation study was done for a varieties the model.  using: small, moderate and large sample sizes, were some new results were obtained. MAPE was used as a statistical criterion for comparison.

The Mishrif Formation is one of the most important geological formations in Iraq consisting of limestone, marl, and shale layers since it is one of the main oil producing reservoirs in the country, which contain a significant portion of Iraq's oil reserves. The formation has been extensively explored and developed by the Iraqi government and international oil companies, with many oil fields being developed within it. The accurate evaluation of the Mishrif formation is key to the successful exploitation of this field. However, its geological complexity poses significant challenges for oil production, requiring advanced techniques to accurately evaluate its petrophysical properties.

   This study used advanced well-logging analysi

The goal of this work is demonstrating, through the gradient observation of a   of type linear ( -systems), the possibility for reducing the effect of any disturbances (pollution, radiation, infection, etc.) asymptotically, by a suitable choice of related actuators of these systems. Thus, a class of  ( -system) was developed based on finite time  ( -system). Furthermore, definitions and some properties of this concept -system and asymptotically gradient controllable system ( -controllable) were stated and studied. More precisely, asymptotically gradient efficient actuators ensuring the weak asymptotically gradient compensation system ( -system) of known or unknown disturbances are examined. Consequently, under convenient hypo

In many applications such as production, planning, the decision maker is important in optimizing an objective function that has fuzzy ratio two functions which can be handed using fuzzy fractional programming problem technique. A special class of optimization technique named fuzzy fractional programming problem is considered in this work when the coefficients of objective function are fuzzy. New ranking function is proposed and used to convert the data of the fuzzy fractional programming problem from fuzzy number to crisp number so that the shortcoming when treating the original fuzzy problem can be avoided. Here a novel ranking function approach of ordinary fuzzy numbers is adopted for ranking of triangular fuzzy numbers with simpler an

The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. The measurement results demonstrate a highly efficient capability for target detection in terms of frequency response and peak forming that was isola

A method is developed for the determination of iron (III) in pharmaceutical preparations by coupling cloud point extraction (CPE) and UV-Vis spectrophotometry. The method is based on the reaction of Fe(III) with excess drug ciprofloxacin (CIPRO) in dilute H2SO4, forming a hydrophobic Fe(III)- CIPRO complex which can be extracted into a non-ionic surfactant Triton X-114, and iron ions are determined spectrophotometrically at absorption maximum of 437 nm. Several variables which impact on the extraction and determination of Fe (III) are optimized in order to maximize the extraction efficiency and improve the sensitivity of the method. The interferences study is also considered to check the accuracy of the procedure. The results hav

The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.